{"id":"https://openalex.org/W4416251429","doi":"https://doi.org/10.1109/ijcnn64981.2025.11228885","title":"MS-YOLO: Infrared Object Detection for Edge Deployment via MobileNetV4 and SlideLoss","display_name":"MS-YOLO: Infrared Object Detection for Edge Deployment via MobileNetV4 and SlideLoss","publication_year":2025,"publication_date":"2025-06-30","ids":{"openalex":"https://openalex.org/W4416251429","doi":"https://doi.org/10.1109/ijcnn64981.2025.11228885"},"language":null,"primary_location":{"id":"doi:10.1109/ijcnn64981.2025.11228885","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn64981.2025.11228885","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100758148","display_name":"Jiali Zhang","orcid":"https://orcid.org/0009-0004-6426-2883"},"institutions":[{"id":"https://openalex.org/I20382870","display_name":"Missouri University of Science and Technology","ror":"https://ror.org/00scwqd12","country_code":"US","type":"education","lineage":["https://openalex.org/I20382870"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Jiali Zhang","raw_affiliation_strings":["Missouri University of Science and Technology,Dept. of Mathematics &#x0026; Statistics,Rolla,MO,USA,65409"],"affiliations":[{"raw_affiliation_string":"Missouri University of Science and Technology,Dept. of Mathematics &#x0026; Statistics,Rolla,MO,USA,65409","institution_ids":["https://openalex.org/I20382870"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Thomas S. White","orcid":null},"institutions":[{"id":"https://openalex.org/I20382870","display_name":"Missouri University of Science and Technology","ror":"https://ror.org/00scwqd12","country_code":"US","type":"education","lineage":["https://openalex.org/I20382870"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Thomas S. White","raw_affiliation_strings":["Missouri University of Science and Technology,Dept. of Computer Science,Rolla,MO,USA,65409"],"affiliations":[{"raw_affiliation_string":"Missouri University of Science and Technology,Dept. of Computer Science,Rolla,MO,USA,65409","institution_ids":["https://openalex.org/I20382870"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5013050862","display_name":"Haoliang Zhang","orcid":"https://orcid.org/0000-0002-3270-514X"},"institutions":[{"id":"https://openalex.org/I8692664","display_name":"University of Oklahoma","ror":"https://ror.org/02aqsxs83","country_code":"US","type":"education","lineage":["https://openalex.org/I8692664"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Haoliang Zhang","raw_affiliation_strings":["University of Oklahoma,School of Electrical &#x0026; Computer Engineering,Norman,OK,USA,73019"],"affiliations":[{"raw_affiliation_string":"University of Oklahoma,School of Electrical &#x0026; Computer Engineering,Norman,OK,USA,73019","institution_ids":["https://openalex.org/I8692664"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5082454668","display_name":"Wenqing Hu","orcid":"https://orcid.org/0000-0002-6116-9104"},"institutions":[{"id":"https://openalex.org/I20382870","display_name":"Missouri University of Science and Technology","ror":"https://ror.org/00scwqd12","country_code":"US","type":"education","lineage":["https://openalex.org/I20382870"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Wenqing Hu","raw_affiliation_strings":["Missouri University of Science and Technology,Dept. of Mathematics &#x0026; Statistics,Rolla,MO,USA,65409"],"affiliations":[{"raw_affiliation_string":"Missouri University of Science and Technology,Dept. of Mathematics &#x0026; Statistics,Rolla,MO,USA,65409","institution_ids":["https://openalex.org/I20382870"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5038037619","display_name":"Donald C. Wunsch","orcid":"https://orcid.org/0000-0002-9726-9051"},"institutions":[{"id":"https://openalex.org/I20382870","display_name":"Missouri University of Science and Technology","ror":"https://ror.org/00scwqd12","country_code":"US","type":"education","lineage":["https://openalex.org/I20382870"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Donald C. Wunsch","raw_affiliation_strings":["Missouri University of Science and Technology,Kummer Institute Center for Artificial Intelligence and Autonomous Systems,Rolla,MO,USA,65409"],"affiliations":[{"raw_affiliation_string":"Missouri University of Science and Technology,Kummer Institute Center for Artificial Intelligence and Autonomous Systems,Rolla,MO,USA,65409","institution_ids":["https://openalex.org/I20382870"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100414617","display_name":"Jian Liu","orcid":"https://orcid.org/0000-0001-6601-5748"},"institutions":[{"id":"https://openalex.org/I20382870","display_name":"Missouri University of Science and Technology","ror":"https://ror.org/00scwqd12","country_code":"US","type":"education","lineage":["https://openalex.org/I20382870"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jian Liu","raw_affiliation_strings":["Missouri University of Science and Technology,Kummer Institute Center for Artificial Intelligence and Autonomous Systems,Rolla,MO,USA,65409"],"affiliations":[{"raw_affiliation_string":"Missouri University of Science and Technology,Kummer Institute Center for Artificial Intelligence and Autonomous Systems,Rolla,MO,USA,65409","institution_ids":["https://openalex.org/I20382870"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5100758148"],"corresponding_institution_ids":["https://openalex.org/I20382870"],"apc_list":null,"apc_paid":null,"fwci":1.163,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.84435542,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":96,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"8"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.8876000046730042,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.8876000046730042,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":0.01080000028014183,"subfield":{"id":"https://openalex.org/subfields/2214","display_name":"Media Technology"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11019","display_name":"Image Enhancement Techniques","score":0.009100000374019146,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/boosting","display_name":"Boosting (machine learning)","score":0.6836000084877014},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.6653000116348267},{"id":"https://openalex.org/keywords/software-deployment","display_name":"Software deployment","score":0.6029000282287598},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.5748999714851379},{"id":"https://openalex.org/keywords/overhead","display_name":"Overhead (engineering)","score":0.5055999755859375},{"id":"https://openalex.org/keywords/enhanced-data-rates-for-gsm-evolution","display_name":"Enhanced Data Rates for GSM Evolution","score":0.41440001130104065},{"id":"https://openalex.org/keywords/anomaly-detection","display_name":"Anomaly detection","score":0.3785000145435333},{"id":"https://openalex.org/keywords/computational-complexity-theory","display_name":"Computational complexity theory","score":0.376800000667572}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6894999742507935},{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.6836000084877014},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.6653000116348267},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6272000074386597},{"id":"https://openalex.org/C105339364","wikidata":"https://www.wikidata.org/wiki/Q2297740","display_name":"Software deployment","level":2,"score":0.6029000282287598},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5770000219345093},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.5748999714851379},{"id":"https://openalex.org/C2779960059","wikidata":"https://www.wikidata.org/wiki/Q7113681","display_name":"Overhead (engineering)","level":2,"score":0.5055999755859375},{"id":"https://openalex.org/C162307627","wikidata":"https://www.wikidata.org/wiki/Q204833","display_name":"Enhanced Data Rates for GSM Evolution","level":2,"score":0.41440001130104065},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.3971000015735626},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.3785000145435333},{"id":"https://openalex.org/C179799912","wikidata":"https://www.wikidata.org/wiki/Q205084","display_name":"Computational complexity theory","level":2,"score":0.376800000667572},{"id":"https://openalex.org/C14036430","wikidata":"https://www.wikidata.org/wiki/Q3736076","display_name":"Function (biology)","level":2,"score":0.3582000136375427},{"id":"https://openalex.org/C97256817","wikidata":"https://www.wikidata.org/wiki/Q1462316","display_name":"Spurious relationship","level":2,"score":0.337799996137619},{"id":"https://openalex.org/C193536780","wikidata":"https://www.wikidata.org/wiki/Q1513153","display_name":"Edge detection","level":4,"score":0.33009999990463257},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.31119999289512634},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.30399999022483826},{"id":"https://openalex.org/C111335779","wikidata":"https://www.wikidata.org/wiki/Q3454686","display_name":"Reduction (mathematics)","level":2,"score":0.302700012922287},{"id":"https://openalex.org/C138236772","wikidata":"https://www.wikidata.org/wiki/Q25098575","display_name":"Edge device","level":3,"score":0.3009999990463257},{"id":"https://openalex.org/C12725497","wikidata":"https://www.wikidata.org/wiki/Q810247","display_name":"Baseline (sea)","level":2,"score":0.2996000051498413},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.28380000591278076},{"id":"https://openalex.org/C2778999744","wikidata":"https://www.wikidata.org/wiki/Q7208292","display_name":"Point target","level":3,"score":0.267300009727478},{"id":"https://openalex.org/C2992147540","wikidata":"https://www.wikidata.org/wiki/Q1277161","display_name":"Adverse weather","level":2,"score":0.2605000138282776},{"id":"https://openalex.org/C2777735758","wikidata":"https://www.wikidata.org/wiki/Q817765","display_name":"Path (computing)","level":2,"score":0.2563000023365021}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn64981.2025.11228885","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn64981.2025.11228885","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320338294","display_name":"Air Force Research Laboratory","ror":"https://ror.org/02e2egq70"},{"id":"https://openalex.org/F4320338295","display_name":"Army Research Laboratory","ror":"https://ror.org/011hc8f90"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":24,"referenced_works":["https://openalex.org/W639708223","https://openalex.org/W1536680647","https://openalex.org/W1861492603","https://openalex.org/W2088049833","https://openalex.org/W2109255472","https://openalex.org/W2120419212","https://openalex.org/W2124386111","https://openalex.org/W2164598857","https://openalex.org/W2193145675","https://openalex.org/W2883780447","https://openalex.org/W2963037989","https://openalex.org/W2982083293","https://openalex.org/W3011722050","https://openalex.org/W3034959114","https://openalex.org/W3035414587","https://openalex.org/W3120093105","https://openalex.org/W4380853827","https://openalex.org/W4385445509","https://openalex.org/W4390492615","https://openalex.org/W4391261800","https://openalex.org/W4394625862","https://openalex.org/W4399946473","https://openalex.org/W4401485877","https://openalex.org/W4401726555"],"related_works":[],"abstract_inverted_index":{"Infrared":[0],"imaging":[1],"has":[2],"emerged":[3],"as":[4,28,62],"a":[5,109],"robust":[6],"solution":[7],"for":[8,169],"urban":[9,174],"object":[10],"detection":[11,160],"under":[12],"low-light":[13],"and":[14,33,70,80,117,138],"adverse":[15],"weather":[16],"conditions,":[17],"offering":[18],"significant":[19],"advantages":[20],"over":[21],"traditional":[22],"visible-light":[23],"cameras.":[24],"However,":[25],"challenges":[26],"such":[27],"class":[29],"imbalance,":[30],"thermal":[31],"noise,":[32],"computational":[34,96,164],"constraints":[35],"can":[36],"significantly":[37],"hinder":[38],"model":[39],"performance":[40],"in":[41,173],"practical":[42],"settings.":[43],"To":[44],"address":[45],"these":[46],"issues,":[47],"we":[48,76,106],"evaluate":[49],"multiple":[50],"YOLO":[51],"variants":[52],"on":[53,73,83,126],"the":[54,91,127,154],"FLIR":[55,128],"ADAS":[56,129],"V2":[57,130],"dataset,":[58],"ultimately":[59],"selecting":[60],"YOLOv8":[61],"our":[63],"baseline":[64],"due":[65],"to":[66],"its":[67],"balanced":[68],"accuracy":[69],"efficiency.":[71],"Building":[72],"this":[74],"foundation,":[75],"present":[77],"MS-YOLO":[78,134,151],"(MobileNetv4":[79],"SlideLoss":[81],"based":[82],"YOLO),":[84],"which":[85],"replaces":[86],"YOLOv8\u2019s":[87],"CSPDarknet":[88],"backbone":[89],"with":[90],"more":[92],"efficient":[93],"MobileNetV4,":[94],"reducing":[95],"overhead":[97],"by":[98],"1.5%":[99],"while":[100,141,162],"sustaining":[101],"high":[102,159],"accuracy.":[103],"In":[104],"addition,":[105],"introduce":[107],"SlideLoss,":[108],"novel":[110],"loss":[111],"function":[112],"that":[113,133,150],"dynamically":[114],"emphasizes":[115],"under-represented":[116],"occluded":[118],"samples,":[119],"boosting":[120],"precision":[121,140],"without":[122],"sacrificing":[123],"recall.":[124],"Experiments":[125],"benchmark":[131],"show":[132],"attains":[135],"competitive":[136],"mAP":[137],"superior":[139],"operating":[142],"at":[143],"only":[144],"6.7":[145],"GFLOPs.":[146],"These":[147],"results":[148],"demonstrate":[149],"effectively":[152],"addresses":[153],"dual":[155],"challenge":[156],"of":[157],"maintaining":[158],"quality":[161],"minimizing":[163],"costs,":[165],"making":[166],"it":[167],"well-suited":[168],"real-time":[170],"edge":[171],"deployment":[172],"environments.":[175]},"counts_by_year":[{"year":2026,"cited_by_count":1}],"updated_date":"2026-04-21T08:09:41.155169","created_date":"2025-11-14T00:00:00"}
